AgentOps
AgentOps is an observability, evaluation, and debugging platform for AI agents. Its open-source Python SDK (with TypeScript support for OpenAI Agents) instruments agent runs in two lines of code, capturing LLM calls, tool invocations, costs, latencies, and multi-agent interactions. Sessions are visualized in a hosted dashboard at app.agentops.ai with time-travel debugging, waterfall views, and replay. AgentOps offers native integrations with 400+ LLMs and frameworks including CrewAI, AutoGen / AG2, LangChain, LangGraph, LlamaIndex, OpenAI Agents, Haystack, and Camel AI.
More than an index entry, but the surface is still mostly links rather than artifacts — the cohort most likely to move a full band from modest, well-targeted work.
API Evangelist profiles AgentOps the way a machine reads it — 35 machine-readable artifacts across 3 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — AgentOps scores 24.9/100 (emerging), with a separate agent-readiness read of 7/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile AgentOps
Each block below is one kind of artifact we hold for AgentOps. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 3
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
AgentOps Python SDK
The AgentOps Python SDK is the primary entry point, installable via pip install agentops and initialized with two lines of code. It auto-instruments supported agent frameworks a...
AgentOps TypeScript SDK
AgentOps' TypeScript SDK provides instrumentation for the OpenAI Agents SDK in Node.js applications, surfacing the same traces and metrics as the Python SDK inside the AgentOps ...
AgentOps Dashboard
The hosted dashboard at app.agentops.ai visualizes agent sessions with waterfall views, time-travel replay, LLM cost tracking, and multi-agent interaction graphs. Supports sessi...
Pricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Agentops Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Agentops Finops
FINOPSFeatures 8
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Two-Line Instrumentation
Initialize observability with agentops.init() and automatic framework instrumentation.
Session Replay
Time-travel debugging with full session and event replay in the dashboard.
LLM Cost Tracking
Token counting and cost tracking across foundation model providers and agents.
Multi-Agent Visualization
Visualize interactions between agents in CrewAI, AutoGen, LangGraph, and custom systems.
Waterfall Traces
Time-based waterfall views of all events in a session.
Custom Traces
Use the @trace decorator and OTel-aligned spans to instrument custom code paths.
Self-Hosting
Self-hosted deployment available on Enterprise plans.
SOC 2 / HIPAA
Enterprise compliance with SOC 2 and HIPAA available on the Enterprise tier.
Scroll within the panel for all 8 ·
Security Posture 1
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Use Cases 5
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Agent Debugging
Inspect multi-step agent runs, tool calls, and intermediate reasoning to find failures.
Cost Monitoring
Track token usage and cost per agent, framework, and provider.
Agent Evaluation
Evaluate agent performance across sessions and compare versions.
Production Observability
Monitor production agents with dashboards, alerts, and exports.
Multi-Agent Systems
Visualize and debug coordination between agents in multi-agent frameworks.
Integrations 15
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
OpenAI
Native instrumentation for OpenAI Chat Completions and Responses APIs.
OpenAI Agents SDK
First-class support for OpenAI Agents in Python and TypeScript.
Anthropic
Instrumentation for Anthropic Claude models.
CrewAI
Native CrewAI integration with multi-agent visualization.
AG2 (AutoGen)
Native integration with AG2, formerly AutoGen.
LangChain
Instrumentation for LangChain chains and agents.
LangGraph
Trace and visualize LangGraph stateful agents.
LlamaIndex
Trace LlamaIndex RAG and agent applications.
Haystack
Instrumentation for Haystack pipelines.
Camel AI
Native integration with Camel AI multi-agent system.
Cohere
Instrumentation for Cohere model calls.
LiteLLM
Capture calls routed through LiteLLM across providers.
Mistral
Instrumentation for Mistral models.
Google Generative AI
Instrumentation for Gemini and Vertex AI.
xAI
Instrumentation for xAI Grok models.
Scroll within the panel for all 15 ·
Resources
Every other property we hold for AgentOps — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 1
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Build 2
SDKs, sample code, and the tooling you integrate with
Access & Security 1
Authentication, authorization, and security posture
Learn 1
Tutorials, courses, talks, and written guidance
Operate 1
Status, limits, changes, and where to get help
Commercial 2
Pricing, plans, and the legal terms of use
Company 3
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/agentops · machine-readable index on apis.io